• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.85-92
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• 2022

In this study, we aim to design an action game that increases the user experience. In order to increase the immersion of the game, the characteristics of the game used by the user were analyzed, and the systemic and visual characteristics of the game were designed with reference to each characteristic. The proposed method uses Unity 3D to implement an interaction system between objects in the game and is designed in a way that allows users to immerse themselves in the game. To induce immersion through the visual elements of the game, 2D objects and players are placed in a 3D space, and a 2D dynamic light shader is added. It is composed of inter-combat rules and monster behavior pattern collision detection and event detection. The proposed method contained the user experience with the implementation thesis, and showed the game's possibility of leading the user's affordance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.199-202
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• 2023

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. Automated testing can help to quickly identify CRLF vulnerabilities, and is particularly useful for companies to test their applications before releasing them. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Security automation is an important issue for companies trying to protect themselves against security threats. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance their security.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Korean Journal of Psychosomatic Medicine
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• v.22 no.1
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• pp.13-22
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• 2014

Objectives : Breast cancer has been the most prevalent female cancer in South Korea since 2001. Early detection of this disease is the most effective strategy for reducing mortality. The objective of this study was to identify factors which could predict advanced stage at diagnosis of breast cancer. Methods : Participants who were initially diagnosed with breast cancer and referred to the Stress Clinic of the Breast Cancer Center at Kyungpook National University Hospital were included. Through a semi-structured interview, the authors investigated psychosocial variables such as the extent of marital and family functioning and emotional-economic family burden as well as sociodemographic and health behavior-, health characteristic- and cancer-related variables. Results : Data were collected from 219 participants. One hundred and twenty(54.8%) subjects were diagnosed with advanced-stage breast cancer. Variables that were significantly different between the advanced-stage and early-stage groups included : monthly breast self examination(p<0.000), annual mammographic screening(p<0.000), mode of tumor detection(p<0.000), nature of the first symptoms(p<0.000), time to treatment after diagnosis(p<0.000), overloaded economic and family burden(p=0.018), marital functioning(p<0.000) and family functioning(p<0.00). Logistic regression analysis indicated that irregular annual mammography screening(OR=7.431 ; 95% CI 2.407-22.944) or a lack of screening(OR=25.299 ; 95% CI 7.855-81.482) and a dysfunctional marital relationship(OR=4.772 ; 95% CI 2.244-10.145) were significantly associated with advanced stage at diagnosis of breast cancer. Conclusions : We reconfirmed screening behavior to be a risk factor for delayed diagnosis of breast cancer. Our findings also emphasized the importance of psychosocial factors such as marital functioning in early detection of breast cancer. Psychiatric consultation in the area of martial functioning could be beneficial for increasing early detection in breast cancer.

• Composites Research
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• v.37 no.4
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• pp.343-349
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• 2024

The use of composite materials for structural fasteners is increasingly common, making it crucial to assess the deformation of these fasteners under fatigue behavior. In this study, clamp-type fasteners were manufactured using carbon fiber reinforced composites, and their structural stability and sectional damage rates were evaluated using electrical resistance measurement during fatigue behavior. While clamp-type composite fasteners exhibited minimal deformation in flat sections, significant deformation occurred in the bent sections due to fatigue. It was observed that insufficient angular stability led to concentrated damage in the bent sections. The dynamic fatigue behavior showed that the length change rate of the composite fasteners was within 0.6%, but the angular change rate reached up to 6%, indicating that the bent sections are the most critical areas. By utilizing the self-sensing capability of the composite fasteners, sectional damage behavior was assessed through electrical resistance measurement. Significant damage was noted in the bent sections due to fatigue, and 3D-CT results revealed substantial deformation and interfacial damage when the initial bend angle of the fasteners was less than 90 degrees. These findings highlight the importance of reinforcing the stiffness of the bent sections and establishing systematic angular standards in the development of composite fasteners.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1037-1046
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• 2024

The widespread adoption of the Internet of Things (IoT) has enhanced efficiency and convenience across various fields, but it has also led to a surge in security threats. Among these, IoT botnets are particularly concerning as they can rapidly infect a large number of devices and launch various types of attacks, making them a significant security threat. In IoT environments where implementing security measures on individual devices is challenging, establishing a security monitoring system for real-time detection and response is essential to mitigate the risks posed by botnets. In the field of security monitoring, it is crucial not only to detect botnets but also to analyze their detailed behaviors to devise effective countermeasures. Security experts devote considerable effort to analyzing the payloads of detected threats to understand botnet behavior and develop appropriate responses. However, analyzing all threats manually is time-consuming and costly. To address this, our study proposes an XAI-based network feature extraction methodology to enhance the effectiveness of IoT botnet behavior analysis. This study proposes a practical security monitoring methodology for IoT botnet behavior analysis and response, consisting of three steps: 1) BPE and TF-IDF based payload feature extraction, 2) XAI-based feature importance analysis, and 3) visualization of decision rationale based on feature importance. This approach provides security experts with intuitive visual evidence of IoT attacks and reduces analysis time, contributing to faster decision-making and response strategy development in security monitoring.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.25-38
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• 2017

Many security threats are occurring around the world due to the characteristics of industrial control systems that can cause serious damage in the event of a security incident including major national infrastructure. Therefore, the industrial control system network traffic should be analyzed so that it can identify the attack in advance or perform incident response after the accident. In this paper, we research the visualization technique as network forensics to enable reasonable suspicion of all possible attacks on DNP3 control system protocol, and define normal action based rules and derive visualization requirements. As a result, we developed a visualization tool that can detect sudden network traffic changes such as DDoS and attacks that contain anormal behavior from captured packet files on industrial control system network. The suspicious behavior in the industrial control system network can be found using visualization tool with Digital Bond packet.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.347-350
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• 2007

An ECG analysis with activity monitoring for the home care of elderly persons or patients, using wireless sensors technology was design and implemented. The changes in heart rate occur before, during, or following behavior such as posture changes, walking and running. Therefore, it is often very important to record heart rate along with posture and behavior, for continuously monitoring a patient's cardiovascular regulatory system during their daily life activity. The ECG and accelerometer data are continuously recorded with a built-in automatic alarm detection system, for giving early alarm signals even if the patient is unconscious or unaware of cardiac arrhythmias. The hardware allows data to be transmitted wirelessly from on-body sensors to a base station attached to server PC using IEEE802.15.4. If any abnormality un at server then the alarm condition sends to the doctor' PDA (Personal Digital Assistant).